Systems and Methods for Data-Driven Identification of Talent

ABSTRACT

The present disclosure describes a talent-identification system that can be used by companies to assist in the recruitment process for new employees. Additionally, the system can be used by job seekers to determine ideal career fields and industries. The system employs an array of neuroscience-based tests to assess a user&#39;s career propensities, after which the system can provide career recommendations to the user or report on employment suitability of the user to a company.

CROSS REFERENCE

This application is a continuation application of U.S. patentapplication Ser. No. 14/751,943, filed on Jun. 26, 2015, whichapplication claims the benefit of U.S. Provisional Patent ApplicationNo. 62/101,524, filed on Jan. 9, 2015, and U.S. Provisional PatentApplication No. 62/018,459, filed on Jun. 27, 2014, the content of eachof which is incorporated herein by reference in their entirety.

BACKGROUND

Recruiting suitable candidates for a position can be a challenging taskfor companies. Generally, companies can rely on recruiters andinterviews to determine if an applicant would be an ideal fit for theirteam. However, finding new employees can be a time-consuming, costly,and, in some cases, futile process, especially if the pool of applicantsis large. Conversely, determining a suitable career path can be adaunting task for new job-seekers, and existing job search resources areoften not tailored to an individual. A platform to find an idealemployee or job, based on a desired characteristic profile, remainsunavailable.

SUMMARY OF THE INVENTION

In some embodiments, the invention provides a computer program productcomprising a computer-readable medium having computer-executable codeencoded therein, the computer-executable code adapted to be executed toimplement a method comprising: a) providing a recruitment system,wherein the recruitment system comprises: i) a task module; ii) ameasurement module; iii) an assessment module; and iv) an identificationmodule; b) providing by the task module a computerized task to asubject; c) measuring by the measurement module a performance valuedemonstrated by the subject in performance of the task; d) assessing bythe assessment module a trait of the subject based on the measuredperformance value; and e) identifying to a hiring officer by theidentification module based on the assessed trait that the subject issuitable for hiring by an entity.

In some embodiments, the invention provides a computer program productcomprising a computer-readable medium having computer-executable codeencoded therein, the computer-executable code adapted to be executed toimplement a method comprising: a) providing a talent identificationsystem, wherein the talent identification system comprises: i) a taskmodule; ii) a measurement module; iii) an assessment module; iv) anidentification module; and v) an output module; b) providing by the taskmodule a computerized task to a subject; c) measuring by the measurementmodule a performance value demonstrated by the subject in performance ofa task; d) assessing by the assessment module a trait of the subjectbased on the measured performance value; e) identifying by theidentification module a career propensity based on the assessing of thetrait of subject; and f) outputting by the output module the identifiedcareer propensity to a hiring officer.

In some embodiments, the invention provides a method comprising: a)providing a computerized task to a subject; b) measuring a performancevalue demonstrated by the subject in performance of the task; c)assessing a trait of the subject based on the performance value; d)comparing by a processor of a computer system the trait of the subjectwith a database of test subjects; e) determining based on the comparingthat the subject is suitable for hiring by an entity; and f) reportingto a hiring officer at the entity that the subject is suitable forhiring.

In some embodiments, the invention provides a method comprising: a)providing a computerized task to a subject; b) measuring a performancevalue demonstrated by the subject in performance of the task; c)assessing a trait of the subject based on the performance value; d)identifying by a processor of a computer system a career propensity ofthe subject based on a comparison of the assessed trait of the subjectwith a database of test subjects; and e) outputting a result of thecomparison to a hiring officer.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts an overview of a modeling system of the invention.

FIG. 2 is a graphical representation of employee participation in anexample of the invention.

FIG. 3 shows the accuracy of models generated by a system of theinvention.

FIG. 4 is a block diagram illustrating a first example architecture of acomputer system that can be used in connection with example embodimentsof the present invention.

FIG. 5 is a diagram illustrating a computer network that can be used inconnection with example embodiments of the present invention.

FIG. 6 is a block diagram illustrating a second example architecture ofa computer system that can be used in connection with exampleembodiments of the present invention.

FIG. 7 illustrates a global network that can transmit a product of theinvention.

DETAILED DESCRIPTION

Companies often rely on inefficient recruiting practices, which can leadto the hiring of weak applicants, and ultimately, lower employeeretention. Further, because the recruiting process can be expensive,employers can be reluctant to acquire new talent. This reluctance canlead to company stagnation and to the departure of top employees topursue better opportunities. Thus, companies are faced with thedifficult task of cost-effective, but accurate hiring. Conversely, newgraduates or job seekers face challenges in finding a career that ismost suited to their talents and inclinations not only owing to anunpredictable job market, but also to the difficulty of initiallydetermining what career path to pursue.

A system of the present invention can be used by companies to identifytalent that is tailored to the company's needs for a specific position.The system can use neuroscience-based tasks to optimize the company'srecruiting and candidate sourcing process. In addition to being a usefulrecruiting tool for companies, the system can also assist individuals incareer-planning and talent identification. By using tests that measure awide array of emotional and cognitive traits, the system can ascertainthe strengths and weaknesses of a user and apply that information todetermine what industry is best matched for the user.

A system of the present invention can use performance-based games tocollect information about a person's cognitive and emotional traits. Thesystem can create an employee profile for a specific company byevaluating current employee performance on the neuroscience tests. Theresults of the neuroscience tests, in combination with performance dataof the employee from the company, can be used to create an idealemployee model. Candidates can then be asked to complete the same tasks,and the candidates' results can be compared to those of currentemployees to determine suitability for a specific position. Candidatescan also be compared across multiple positions to ascertain whichposition, if any, is suitable based on the profile created by thesystem.

Methods of a System of the Invention.

A wide range of rigorous methods can be used by a system of theinvention to discover pertinent information for predicting factors aboutsubjects that are of interest to a company. The system's assessment cancomprise collecting objective data using the system's assessment module,and then modeling learning behavior dynamics. A strength of modelinglearning behavior dynamics is that instead of examining behavior with astatic score, for example, the average score, the system can insteadexamine behavior over time. This method can allow the system toascertain metrics of learning, for example, how test takers learn fromerrors or how rewards affect the test-takers' learning. These metrics oflearning are often neglected in human capital analytics, but can bevaluable in determining important employee characteristics.

The system can use scores generated by the individual assessments withinthe system to create a fit score for a subject. The fit score can be anaggregation of the scores of the individual tasks. The fit score canrange from 0-100% and predict the likelihood that a subject would besuitable for a specific position or career industry. A fit score can be,for example, about 0%, about 1%, about 2%, about 3%, about 4%, about 5%,about 6%, about 7%, about 8%, about 9%, about 10%, about 15%, about 20%,about 25%, about 30%, about 35%, about 40%, about 45%, about 50%, about60%, about 70%, about 80%, about 90%, or about 100%.

Prior to performing prediction analyses, the system can quantify therelationships in existing data, and the quantification can identify themain features of the data and provide a summary of the data. Forexample, before the system can predict whether a particular candidatecan succeed at a specific company as a management consultant, the systemcan build a descriptive model of the relationship between the currentemployees' traits and their success as management consultants. Thesystem's analytics engine can implement various data mining andclustering algorithms for unsupervised classification to generate thesedescriptive models. To create descriptive models, the system can takeassessment data from current employees and correlate the data withratings of the employees provided to the system by the company. Theseratings can be objective metrics, such as those used in performancereviews, and of particular interest to the company.

FIG. 1 is an overview of how an analytics engine of the system can beused as a predictive model for a business entity attempting to predicthow likely a potential hire will succeed as an employee. In the firststep, a current employee can complete the tests of the system. Uponcompletion of the tests, the system can extract cognitive and emotionaltrait data based on the performance of the employee on the tests. Next,the system can use the employee's rating data and test data to train theanalytics engine to determine what characteristics an ideal employeeshould possess for a specific position at the business entity.

Once the analytics engine is adequately trained, the model can be usedin the second step for predictive analysis and forecasting. First, thecandidate can complete the system's tests. Upon completion, the systemcan extract traits about the candidate based upon the candidate'sperformance on the tests. The data from the tests can then be applied tothe trained analytics engine to create a fit score for the candidate.These predictive models can be used to assess factors including, forexample, how likely a potential hire would be to succeed in a particularrole at the company. Accurate predictive models can detect subtle datapatterns to answer questions about an employee's future performance inorder to guide employers to optimize their human capital.

A system of the invention can provide a method of providing acomputerized task to a subject. The task can be a neuroscience-basedassessment of emotion or cognition. Upon completion of the tasks, thesystem can measure a performance value of the subject based on thesubject's performance on the task. A specific trait can then be assessedbased on the performance value, wherein the assessed trait can be usedto create a profile for the subject. The trait can then be compared by aprocessor of a computer system with a database of test subjects.Comparison of the traits of the subject with a database of test subjectcan be used to create a model specific to the tested subject. The modelcan be then used to score the subjects, which can assist in creating aquantitative assessment of the subject's emotion or cognition. The testsubjects can work for a business entity. The comparison of the trait ofthe subject with the database of test subjects can be used to determinewhether the subject is suitable for hiring.

A system of the invention can provide a method of providing acomputerized task to a subject. The task can be a neuroscience-basedassessment of emotion or cognition. Upon completion of the tasks, thesystem can measure a performance value of the subject based on thesubject's performance on the task. A specific trait can then be assessedbased on the performance value, wherein the assessed trait can be usedto create a profile for the subject. The assessed trait can further beused to generate a model of the subject based on assessment of more thanone trait of the subject and comparison of the subject's model and areference model. A processor of a computer system can then be used toidentify the subject's career propensity based on a comparison of thesubject's trait with a database of test subjects. The comparison of thesubject's trait with a database of test subjects can also be used togenerate a model of the subject. The results of the comparison can beoutputted to a hiring officer. The results of the comparison can furtherbe used to recommend careers for the subject.

Non-limiting examples of tasks that can be part of the system includeAnalogical Reasoning, Balloon Analogue Risk Task, Choice Task, DictatorTask, Digit Span, EEfRT, Facial Affect Task, Finger Tapping, FutureDiscounting, Flanker Task, Go/No-Go, Mind in the Eyes, N-Back, PatternRecognition, Reward Learning Task, Tower of London, and the Trust Task.

The system can implement a wide range of machine learning techniques tobuild models that provide the most accurate prediction. A modelgenerated by the system can learn to capture characteristics of interestfrom the training data without knowing the underlying probabilitydistribution. Training data can be seen as examples that illustratespecific relationships between the observed variables. An advantage ofmachine learning is automatic recognition of complex patterns andintelligent decisions based on example data. The system can use, forexample, non-linear, non-parametric classification techniques, which canperform better than traditional pattern classification algorithms indata sets having many attributes with a small training dataset.

Applications of a System of the Invention.

A system of the invention can be used by a business entity to findsubjects to work on behalf of the entity. Non-limiting examples of abusiness entity include a corporation, a cooperative, a partnership, acompany, a public limited company, a private company, a public company,a limited liability company, a limited liability partnership, a chartercorporation, an organization, a non-profit organization, a staffingagency, an academic institution, a government facility, a governmentagency, a military department, and a charitable organization. Users of asystem of the invention can further include, for example, recruiters,human resources personnel, managers, supervisors, hiring officers, andemployment agencies.

Non-limiting examples of subjects who can work on behalf of a businessentity include an employee, a full-time employee, a part-time employee,a statutory employee, a temporary employee, a contractor, an independentcontractor, a subcontractor, an emeritus employee, a consultant, and anadvisor.

A system of the invention can also be used by a subject to determine thesubject's career propensities. Subjects who can use the inventioninclude, for example, students, post-graduates, job seekers, andindividuals seeking assistance regarding career planning. A subject cancomplete the tasks of the system, after which the system can create aprofile for the subject based upon identified traits of the subject. Auser can access a system of the invention from a computer system. Theuser can then complete the computerized tasks of the system using, forexample, a computer, a laptop, a mobile device, or a tablet.

A subject's profile can be compared to a database of test subjects toscore the subject and generate a model for the subject based onreference models. The test subjects can, for example, work for abusiness entity. The system can additionally generate a fit score forthe subject based on the test subjects who work for a business entityand the test subjects' specific positions at the business entity. Asystem of the invention can recommend various industries to a subjectbased upon the subject's determined career propensity. Non-limitingexamples of the industries that can be recommended by the system includeconsulting, education, healthcare, marketing, retail, entertainment,consumer products, entrepreneurship, technology, hedge funds, investmentmanagement, investment banking, private equity, product development, andproduct management.

A system of the invention can use a series of emotional and cognitivetraits to determine a subject's talents and propensity for differentcareer fields. The emotional traits that can be measured by a system ofthe invention include, for example, trust, altruism, perseverance, riskprofile, learning from feedback, learning from mistakes, creativity,tolerance for ambiguity, ability to delay gratification, rewardsensitivity, emotional sensitivity, and emotional identification. Thecognitive traits that can be measured by a system of the inventioninclude, for example, processing speed, pattern recognition, continuousattention, ability to avoid distraction, impulsivity, cognitive control,working memory, planning, memory span, sequencing, cognitiveflexibility, and learning.

Emotional traits can be important factors in determining whether asubject will be suitable for the company, and a specific role within thecompany. A system of the invention can assess a variety of emotionaltraits to assist a user of the system in making decisions.

Trust can be evaluated as a willingness to rely upon another's actionswithout knowledge of the other's actions. Trust can demonstrate whetherthe subject can work effectively in a group setting, and rely on others'opinions and actions.

Altruism can be assessed as selflessness, or the willingness to performactions for the welfare of others. Altruism can demonstrate that thesubject can be more willing to serve the needs of the company than theneeds of the self.

Perseverance can be described as continuing on a course of actionwithout regard to discouragement. Perseverance can demonstrate that evenin times of failure or opposition, the subject can find a solution andfocus on assigned tasks.

Creativity can demonstrate that the subject can have unconventionalapproaches for solving problems and performing tasks.

A risk profile for a candidate can identify the willingness of a subjectto take risks. A subject who is more willing to take risks can be morefavorable for a company that deals with high-risk, high-pressuresituations.

Learning from feedback can measure whether a subject can use suggestionsfrom others to modify behaviors or actions while performing a functionof a job. Learning from mistakes can assess whether a subject can usemistakes made on a task to modify future behavior to perform the sametask.

A tolerance for ambiguity can assess a subject's comfort level withuncertain or incomplete situations, and stimuli, and the subject'sreactions to the same. A subject with a tolerance for ambiguity can bemore creative and resourceful when faced with incomplete or questionabledata.

A subject with an inclination toward delayed gratification can appeal toa company because the subject can work harder, and for a longer periodtime, in expectation of a raise or bonus.

Reward sensitivity is related to delayed gratification in that rewardsensitivity can measure how motivated a subject is by the promise of areward. A company can desire a subject who is not only intrinsicallymotivated, but also sensitive to rewards, such as raises and bonuses.

Emotional sensitivity and identification can describe whether a subjectis able to respond to another's emotions in an appropriate manner, andwhether the subject is able to identify correctly the emotions ofanother. Subjects with higher emotional sensitivity and identificationabilities can be better team players and leaders.

In addition to the emotional traits that can be measured by a system ofthe invention, cognitive traits can also be assessed and used by abusiness entity to determine whether a subject is suitable foremployment.

Processing speed relates to the ability to process informationthoroughly and speedily, without the need for intentional thought. Asubject with a higher processing speed can be desirable to a company inthat the subject can think and react to situations quickly.

Pattern recognition can refer to the ability to recognize a set ofstimuli arranged in a certain manner that is characteristic of that setof stimuli. A subject with higher pattern recognition skills candemonstrate better critical thinking skills and identify trends in data.

A subject with a higher continuous attention score can demonstrate ahigher ability to sustain attention on a single task. A subject can alsobe assessed for the ability to avoid distraction, and focus on specifictasks.

Impulsivity can be evaluated as performing actions without foresight,reflection, or consideration of consequences. A subject who is impulsivecan be viewed unfavorably by a potential employer, as the subject canmake rash decisions that can prove disadvantageous for the company. Animpulsive subject can also be viewed favorably if the company desires asubject more willing to take risks, think creatively, and act quickly.

Cognitive control can describe a variety of cognitive processesincluding working memory, learning, cognitive flexibility, and planning.Working memory is the active part of the memory system and can involveboth short-term memory and attention. A subject with high working memorycan display more focused attention to a task and the ability tomulti-task.

Cognitive flexibility can be described as the ability to switch fromdifferent tasks and to think about multiple tasks simultaneously andeffectively. A subject with cognitive flexibility can balance many tasksefficiently.

Planning demonstrates an ability to organize actions to achieve a goal,and can demonstrate foresight in the execution of tasks.

Memory span is a measure of short-term memory and can be assessed byhaving a subject recite a series of numbers or words presentedpreviously. A subject with a greater memory span can rememberinstructions and perform a specific task better than someone with ashort memory span.

Sequence learning is the ability to sequence actions and thoughts,without conscious awareness that such sequencing is occurring. Sequencelearning can comprise four sequencing problems. First, sequenceprediction can attempt to predict elements of a sequence based on thepreceding elements. Second, sequence generation can attempt to piecetogether elements of the sequence one-by-one as the elements naturallyoccur. Third, sequence recognition can attempt to ascertain whether thesequence is legitimate based on a pre-determined criteria. Finally,sequence decision-making can involve selecting a sequence of actions toachieve a goal, to follow a trajectory, or to maximize or minimize acost function.

A system of the invention can be used to match an individual or group ofindividuals to another individual or group of individuals for thepurposes of recommending compatibility within the professional orpersonal realm.

Statistical Functions Used in a System of the Invention.

The tests used in a system of the invention can be assessed for theirprecision of measurements. The precision of the tests can be importantfor determining if the tests are accurate predictors of human emotionand cognition. To ascertain the precision of the tests, reliabilityassessments can be performed. One output that can be measured for testreliability is the Pearson's correlation coefficient (r). The Pearson'scorrelation coefficient can describe the linear relationship between tworesults and is between −1 and +1. The correlation coefficient for asample, r, can be calculated using the following formula:

${r = \frac{\sum\limits_{i = 1}^{n}{\left( {X_{i} - \overset{\_}{X}} \right)\left( {Y_{i} - \overset{\_}{Y}} \right)}}{\sqrt{\sum\limits_{i = 1}^{n}\left( {X_{i} - \overset{\_}{X}} \right)^{2}}\sqrt{\sum\limits_{i = 1}^{n}\left( {Y_{i} - \overset{\_}{Y}} \right)^{2}}}},$

where n is the sample size; i=1, 2, . . . , n; X and Y are thevariables, and X and Y are the means for the variables. The square ofthe Pearson's correlation coefficient, r², is known as the coefficientof determination and can be used to explain the fraction of variance inY as a function of X in a simple linear regression.

The Pearson's correlation coefficient can also be used to describeeffect size, which can be defined as the magnitude of the relationshipbetween two groups. When the Pearson's correlation coefficient is usedas a measure for effect size, the square of the result can estimate theamount of the variance within an experiment that is explained by theexperimental model.

Reliability can be an indicator of the extent to which measurements areconsistent over time and free from random error. Reliability can measurewhether the test results are stable and internally consistent. Thetest-retest method is one measure that can be used for reliability.Test-retest reliability test can measure a change in a sample's resultswhen the sample is administered the same test at two different times. Ifthe results from the test given at two different times are similar, thenthe test can be considered reliable. The relationship between the tworesults can be described using the Pearson's correlation coefficient;the higher the value of the correlation coefficient, the higher thereliability of the test.

The value of the correlation coefficient for test-retest reliability canbe, for example, about −1.0, about −0.95, about −0.9, about −0.85, about−0.8, about −0.75, about −0.7, about −0.65, about −0.6, about −0.55,about −0.5, about −0.45, about −0.4, about −0.35, about −0.3, about−0.25, about −0.2, about −0.15, about −0.1, about −0.05, about 0.05,about 0.1, about 0.15, about 0.2, about 0.25, about 0.3, about 0.35,about 0.4, about 0.45, about 0.5, about 0.55, about 0.6, about 0.65,about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.95, orabout 1.0.

Another test that can be used for measuring reliability of a test is thesplit-half reliability test. The split-half reliability test divides atest into two portions, provided that the two portions contain similarsubject matter, and the test is administered to a sample. Then, scoresof each half of the test from the sample are compared to each other. Thecorrelation, or degree of similarity, between the scores from the twohalves of the test can be described using the Pearson's correlationcoefficient, wherein if the correlation is high, the test is reliable.

The value of the correlation coefficient for split-half reliability canbe, for example, about −1.0, about −0.95, about −0.9, about −0.85, about−0.8, about −0.75, about −0.7, about −0.65, about −0.6, about −0.55,about −0.5, about −0.45, about −0.4, about −0.35, about −0.3, about−0.25, about −0.2, about −0.15, about −0.1, about −0.05, about 0.05,about 0.1, about 0.15, about 0.2, about 0.25, about 0.3, about 0.35,about 0.4, about 0.45, about 0.5, about 0.55, about 0.6, about 0.65,about 0.7, about 0.75, about 0.8, about 0.85, about 0.9, about 0.95, orabout 1.0.

Validity is the extent to which a test measures what is intended. For atest to be valid, a test can demonstrate that the results of the testare contextually supported. Specifically, evidence regarding testvalidity can be presented via test content, response processes, internalstructure, relation to other variables, and the consequences of testing.

A Hotelling's T-squared test is a multivariate test that can be employedby a system of the invention to determine the differences in the meansof the results of different populations of subjects using the system.The test statistic (T²) for the T-squared test is calculated using theformula below:

${T^{2} = {\left( {{\overset{\_}{x}}_{1} - {\overset{\_}{x}}_{2}} \right)^{\prime}\left\{ {S_{p}\left( {\frac{1}{n_{1}} + \frac{1}{n_{2}}} \right)} \right\}^{- 1}\left( {{\overset{\_}{x}}_{1} - {\overset{\_}{x}}_{2}} \right)}},$

where x is the sample mean, S_(p) is the pooled variance-covariance ofthe samples, and n is the sample size.

To compute the F-statistic, the following formula is used:

${F = {{\frac{n_{1} + n_{2} - p - 1}{p\left( {n_{1} + n_{2} - 2} \right)}T^{2}} \sim F_{p,{n_{1} + n_{2} - p - 1}}}},$

where p is the number of variables being analyzed, and the F-statisticis F-distributed with p and n₁+n₂−p degrees of freedom. An F-table canbe used to determine the significance of the result at a specified α, orsignificance, level. If the observed F-statistic is larger than theF-statistic found in the table at the correct degrees of freedom, thenthe test is significant at the defined α level. The result can besignificant at a p-value of less than 0.05 if, for example, the α levelwas defined as 0.05.

Analysis of variance (ANOVA) is a statistical test that can be used by asystem of the invention to determine a statistically significantdifference between the means of two or more groups of data. TheF-statistic for ANOVA can be calculated as follows:

${F = \frac{\frac{{n_{1}\left( {{\overset{\_}{x}}_{1} - \overset{\_}{x}} \right)}^{2} + {n_{2}\left( {{\overset{\_}{x}}_{2} - \overset{\_}{x}} \right)}^{2} + \ldots + {n_{I}\left( {{\overset{\_}{x}}_{I} - \overset{\_}{x}} \right)}^{2}}{I - 1}}{\frac{{\left( {n_{1} - 1} \right)s_{1}^{2}} + {\left( {n_{2} - 1} \right)s_{2}^{2}} + \ldots + {\left( {n_{I} - 1} \right)s_{I}^{2}}}{N - I}}},$

where x is the sample mean, n is the sample size, s is the standarddeviation of the sample, I is the total number of groups, and N is thetotal sample size. An F-table is then used to determine the significanceof the result at a specified α level. If the observed F-statistic islarger than the F-statistic found in the table at the specified degreesof freedom, then the test is significant at the defined α level. Theresult can be significant at a p-value of less than 0.05 if, forexample, the α level was defined as 0.05.

The α level for the Hotelling's T-squared test or ANOVA can be set at,for example, about 0.5, about 0.45, about 0.4, about 0.35, about 0.3,about 0.25, about 0.2, about 0.15, about 0.1, about 0.05, about 0.04,about 0.03, about 0.02, about 0.01, about 0.009, about 0.008, about0.007, about 0.006, about 0.005, about 0.004, about 0.003, about 0.002,or about 0.001.

Any tool, interface, engine, application, program, service, command, orother executable item can be provided as a module encoded on acomputer-readable medium in computer executable code. In someembodiments, the invention provides a computer-readable medium encodedtherein computer-executable code that encodes a method for performingany action described herein, wherein the method comprises providing asystem comprising any number of modules described herein, each moduleperforming any function described herein to provide a result, such as anoutput, to a user.

EXAMPLES Example 1 Analogical Reasoning

The Analogical Reasoning Task can measure the ability of a subject todiscern connections between concepts or events that are seeminglyunrelated. Analogical reasoning can further refer to tasks usinganalogies to model novel connections between situations orrepresentations that do not seem similar on the surface. Analogicalreasoning has frequently been linked to creative problem-solving as bothrequire individuals to generate innovative ideas within the constraintsof a particular task. The more disparate two situations appear, the morecreative the analogical reasoning process can be. The likeness betweentwo situations, concepts, events, or representations can be described bysemantic distance. The greater the semantic distance, the lesssimilarity exists between the two presented situations. In theanalogical reasoning task, the semantic distance can be highlycorrelated with independent raters' evaluations of creativity, as in thesubject can be perceived as more creative when the subject forms aconnection between situations that seem highly dissimilar. Functionalmagnetic resonance imaging (fMRI) can be used to measure brain activityduring an analogical reasoning task, and the semantic distance betweenthe items in the analogies can be parametrically varied. Critically,semantic distance of analogical mapping, and not task difficulty, asassayed by response time, correctness, and rated difficulty, canmodulate brain activity.

In the present invention, a subject was presented with two sets of wordpairs and then asked to determine whether the second set was analogousto the relationship between the first set. A system of the presentinvention performed a test-retest study using an undergraduate sample(N=38) with a two-week interval between testing sessions. Thetest-retest reliability of the analogical reasoning task was foundacceptable at about r=0.63.

Example 2 Balloon Analogue Risk Task (BART)

In the BART, subjects earned money in a computer game wherein with eachclick on a cartoon pump, a simulated balloon inflated and a small amountof money was deposited into a temporary bank account. The subjects wereallowed to collect the money at any point. However, if the balloonpopped, the temporary bank account accrued no money and the trial ended.The number of clicks by the subject served as a measure of risk-taking,and the task lasted for about 80 trials.

Performance on a BART can be correlated with several other risk-relatedconstructs including the Barratt Impulsivity Scale, the SensationSeeking Scale, and the Behavioral Constraint scale.

The validity of the BART can be determined by looking at how performanceon the BART correlates to self-report measures completed by the testsubjects. To demonstrate the incremental validity of the BART inpredicting risky behavior, a step-wise regression analysis can be doneusing age, gender, impulsivity, and sensation seeking in step one, andfactoring in the BART results in step two. Regression analysis of stepsone and two can show that even while controlling for other factors,higher BART scores can be linked to a higher propensity for riskybehavior. The BART can be significantly correlated to impulsivity,sensation seeking, and a risk score, while not having a significantcorrelation with other demographic factors.

A test-retest study was done by a system of the invention using anundergraduate sample (N=40) with a two-week interval in between testingsessions. The test-retest reliability was found to range from an r ofabout 0.65 to about 0.88 depending on the level of risk. Another studyconducted on a community sample (N=24) showed that split-halfreliability ranged from an r of about 0.88 to about 0.96, depending onthe level of risk.

Example 3 Choice Task

The Choice Task can be used as a measure of risk-taking inclinations ofa subject. The Choice Task can include a set of scenarios, in whichsubjects are asked to evaluate based on a series of choice sets. Thechoice sets can comprise alternatives that are mutually exclusive andindependent, and generally one alternative can be considered the riskierof the two options. A study can be conducted wherein subjects are askedto complete a variety of tests that measure personality and behavioralrisk measures. Tests that the subjects can complete include Zuckerman'sSensation Seeking Scale, Eysenck's Impulsivity Scale, RetrospectiveBehavioral Self-Control Scale, Domain Specific Risk Taking Scale, ChoiceTask, Balloon Analogue Risk Task, Variance Preference Task, FutureDiscounting I, and Future Discounting II. A principal componentsanalysis can be done to determine which principal components are theunderlying measures of risk. For instance, variance preference can becorrelated with the Choice Task. Variance preference can be a strongmeasure for risk, and can be described as a personality tendency forexcitement and extroversion.

In a system of the invention, subjects were asked if they would eitherreceive a set amount of money or bet on a chance of receiving a higheramount of money. The amounts of money and the chances of receiving themoney were varied to make the options seem more or less risky. Twostudies were undertaken by the system to establish the test-retestreliability of the Choice Task. One study was conducted with anundergraduate sample (N=40) with a two-week interval between testingsessions. The measured test-retest reliability was found to be aboutr=0.62. The second study was a split-half reliability study using acommunity sample (N=24). The split-half reliability was found to beabout r=0.82.

Example 4 Dictator Task

The Dictator Task can be used in behavioral economics as a measure ofgenerosity and altruism. To determine the validity of this game, thesubjects can be asked to report on their philanthropy over the pastyear. For example, subjects that donated their fictional earnings duringthe task can be found to have, in reality, donated more to philanthropiccauses in the past year than those who did not donate their fictionalearnings during the task.

In the present invention, subjects were paired with random participantswhere both the subject and the random participant initially received thesame amount of money. Subsequently, the subject was given an additionalamount of money and instructed to give the random participant none,some, or all of the money. The amount of money donated to the randomparticipant was used as a measure of altruism. A test-retest study wasconducted by a system of the invention using an undergraduate sample(N=40) with a two-week interval in between testing sessions. Thetest-retest reliability was found acceptable at about r=0.62. Thesplit-half reliability was also measured using a community sample (N=24)and the reliability was found acceptable at about r=0.65.

Example 5 Digit Span

The Digit Span task can be used to measure a subject's working memorynumber storage capacity. In a system of the invention, subjects werepresented with a series of digits and, when prompted, asked to repeatthe sequence of digits by entering the digits on a keyboard. If thesubject successfully recited the numbers, then the subject was given alonger sequence to remember and recite. The length of the longest list asubject can remember is the subject's digit span.

Digit Span can be positively correlated with all measures of workingmemory, including measures used to represent capacity and processing,and Digit Span can be negatively correlated with age. The Digit Spantask can have adequate reliability for healthy adults over a one-monthinterval.

Digit Span tests were conducted. In a community sample (N=23), thesplit-half reliability for the Digit Span task was found acceptable atr=0.63. A test-retest study with a two-week interval between testingsessions on an undergraduate sample (N=39) also showed acceptablereliability where r=0.68.

Example 6 EEfRT (Easy or Hard)

The Effort-Expenditure for Rewards Task (EEfRT) can be used to exploreeffort-based decision-making in humans. The EEfRT can measure how mucheffort a person is willing to expend for a reward. Across multipleanalyses, a significant inverse relationship can be observed betweenanhedonia and willingness to expend effort for rewards. Increased traitanhedonia can significantly predict an overall reduced likelihood ofexpending effort for a reward, indicating that the EEfRT task can be aneffective proxy for motivation and effort-based decision-making.

In a system of the invention, subjects were presented with a choice tocomplete an easy or a hard task. The easy task involved pressing thespace bar of a keyboard fewer times than did the hard task. Completionof the easy task guaranteed the same reward every time, whereascompletion of the hard task provided a chance of receiving a much higherreward. Subjects who were more inclined to pick the harder task wereassessed as being more motivated by reward, even when more effort wasrequired.

The system conducted tests on reliability for the EEfRT. In a communitysample (N=24), the split-half reliability for the EEfRT was found to beabove average at r=0.76. A second study was conducted using anundergraduate sample (N=40) with a two-week interval between testingsessions. The test-retest reliability was found acceptable at r=0.68.

Example 7 Facial Affect Test

Situational factors can have a strong influence on a subject'sinterpretation of emotional expression if a facial expression providesrelevant, but unclear information. Within this limited context, mostsubjects can judge the expresser to feel an emotion that matches thesituation, rather than their actual facial expression. Situationalinformation can be especially influential when suggesting a non-basicemotion, for example, a person can be in a painful situation, butdisplay an expression of fear. Often, a subject judging the expressionof the person concludes that the person's expression is that of pain,not of fear.

In a system of the invention, subjects were presented with photographsof men and women displaying different emotions. In some instances, thephotographs were presented with a story describing the situation, whileother photographs were presented alone. The subjects were instructed tochoose from a set of four emotions that best described the expression ofthe person in the photograph. Subject who could correctly identify theemotion without being presented with story were described as having anacute ability to read facial expressions.

The system conducted reliability tests on the Facial Affect Task. Thesplit-half reliability of the Facial Affect task was measured using acommunity sample (N=24). The split-half reliabilities were found aboveaverage, with r values ranging from about 0.73-0.79. An undergraduatesample (N=40) was measured twice, with a two-week interval betweensessions. The test-retest reliability was found acceptable, with rvalues of about 0.57-0.61.

Example 8 Finger Tapping (Keypresses)

The Finger-Tapping test (FTT) is a psychological test that can assessthe integrity of the neuromuscular system and examine motor control. Thetask can have good reliability over a one-month interval.

A simple motor tapping task can be conducted on healthy subjects. Thesubjects can be required to tap a stationary circle on a touch-screenmonitor with the index finger of their dominant hand as fast as possiblefor 60 seconds. The test-retest interval can be about four weeks, andcan have a significantly high reliability correlation.

In a system of the invention, subjects were asked to hit the space barof a keyboard repeatedly using the dominant hand for a specified amountof time. The split-half reliability of the FTT was assessed using acommunity sample (N=24). Key measures were found reliable with r valuesof about 0.68-0.96. A test-retest study used an undergraduate sample(N=40) with an interval of two weeks between testing sessions.Reliabilities for relevant measures were found acceptable, with r valuesbetween about 0.58-0.77.

Example 9 Future Discounting

Temporal future discounting can refer to the extent to which individualsprefer immediate, but modest, rewards to future, but sizeable, rewards.Temporal discounting can be modeled as an exponential function leadingto a monotonic decrease in preference with increased time delay, whereinindividuals discount the value of a future reward by a factor thatincreases with the delay of the reward. Hyperbolic discounting can referto a time-inconsistent model of future discounting. When a hyperbolicmodel is used to model future discounting, the model can suggest thatvaluations fall very rapidly for small delay periods, but then fallslowly for longer delay periods. A hyperbolic curve can show a betterfit than other models, providing evidence that individuals discountdelayed rewards.

In a system of the invention, subjects were presented with questions,wherein the subjects had to choose between receiving a certain amount ofmoney now, or more money at a specified time in the future. The amountof money and time when the money would be given to the subject wasvaried to increase or decrease the delay and size of the reward.

The system conducted reliability tests of the Future Discounting Task.The split-half reliability of the future discounting task was assessedusing a community sample (N=24). The split-half reliability forlog-transformed data was found acceptable at about r=0.65. A test-reststudy assessed the reliability of the future discounting task using asample of undergraduates (N=40), with a two-week interval betweentesting sessions. The reliability of the log-transformed data was foundacceptable at about r=0.72.

Example 10 Flanker Task

The Flanker Task can be used to examine task switching capabilities in asubject. The Flanker Task can refer to a set of response inhibitiontests used to assess the ability to suppress responses that areinappropriate in a particular context. The Flanker Task can be used toassess selective attention and information processing capabilities. Atarget can be flanked by non-target stimuli, which correspond either tothe same directional response (congruent stimuli) as the target, to theopposite response (incongruent stimuli), or to neither (neutralstimuli). Different rules are given to the subject as to how the subjectshould react to what they see.

Consistently poor performance can be observed when subjects are asked toswitch tasks versus repeat a task, showing validity for thetask-switching effects of the flanker task. The anterior cingulatecortex (ACC), which can be more active in response to incongruentstimuli compared to congruent stimuli, can be activated during theFlanker Task and can monitor the amount of conflict in the task. Thelevel of conflict measured by the ACC can provide more control to thesubject on the next trial, indicating that the more conflict presentedin trial n, the more control can be presented by the subject in trialn+1.

The Flanker Task and transcranial magnetic function (TMS) can be used tofind the time course of a post-error adjustment leading to a post-errorslowing (PES). Some results can show that the excitability of the activemotor cortex can decrease after an erroneous response.

In a system of the invention, subjects were instructed to press specificarrow keys on a keyboard depending upon the direction and color of fivepresented arrows. If a red arrow was the central arrow among five redarrows, then the direction of the central red arrow dictated which keyto press. If a red arrow was the central arrow among four blue arrowsthat all pointed in the same direction, then the direction of the bluearrows dictated which key the subject should press. For example, if thesubject was shown a sequence of five red arrows pointing to the right,then the subject should have pressed the right arrow key. If the nextimage showed the red, central arrow pointing to the right, but the restof the red arrows pointed to the left, then the subject should havepressed the right arrow key again. However, if the next image showed thered, central arrow pointing to the right surrounded by blue arrowspointing to the left, then the subject should have pressed the leftarrow key. The ability to push the correct arrow key based upon the“flankers,” or arrows surrounding the central arrow, was used to measurethe task switching abilities in the subject.

The system conducted reliability tests for the Flanker Task. Thesplit-half reliability of the Flanker Task was assessed using acommunity sample (N=14). Key measures were found reliable with r valuesof about 0.70-0.76. In a second study, an undergraduate sample (N=34)was used to assess test-retest reliability. Results for relevantmeasures were found acceptable, with r values of about 0.51-0.69.

Example 11 Go/No-Go

A Go/No-Go test can be used to assess a subject's attention span andresponse control. An example of a Go/No-Go test can include having asubject press a button when a specific stimulus is present (“Go”), andnot pressing the same button when a different stimulus is presented(“No-Go”). Performance on the Go/No-Go task, especially for inhibitiontrials, can be related to complex executive functions measured by theWisconsin Card Sorting Task, Stroop Color-Word Test, and Trail MakingTest.

In a system of the invention, subjects were presented with either a redcircle or a green circle and instructed to press the space bar whenshown the red circle, but press nothing when shown the green circle. Thesplit-half reliability of the Go/No-Go task was studied using acommunity sample (N=23). The split-half reliabilities for relevantmeasures were found acceptable, with r values of about 0.56. Atest-retest study was also conducted on a sample of undergraduates(N=33) with a two-week interval between sessions. The reliability on akey measure was found strong of about r=0.82.

Example 12 Mind in the Eyes

The Mind in the Eyes test can evaluate social cognition in subjects byassessing the subject's ability to recognize the mental state of othersusing just the expressions around the eyes. A series of experimentsvarying the type of emotion, amount of face used as stimuli, and genderof stimuli can be conducted to determine how subjects perceive basic andcomplex emotions. Healthy controls can perceive both basic and complexemotions well from the whole face, but for complex mental states, thesubjects' scores can be higher looking at just the eyes. This findingsuggests that the eyes can hold more information than the whole face.

In a system of the invention, subjects were presented with a series ofphotographs revealing only the eyes of the individuals in thephotographs. The subjects were then instructed to choose the emotionthat they felt was best represented by the eyes. The choices of emotionranged from basic, for example, sad, happy, angry, and surprised, tocomplex, for example, arrogant, regretful, judgmental, and nervous.Subjects who were able to read emotions correctly from the eyes weredescribed as more emotionally perceptive.

The system conducted reliability tests on the Mind in the Eyes task. Thesplit-half reliability of the Mind in the Eyes task was assessed in acommunity sample (N=23), and the split-half reliability had an aboveaverage correlation of about r=0.74. A test-retest study on anundergraduate sample (N=38) with a two-week interval between testingsessions had acceptable reliability of about r=0.67.

Example 13 N-Back (Letters)

The N-back task is a continuous performance task that can be used tomeasure working memory in a subject. For example, a subject can bepresented with a sequence of stimuli, and the subject must indicate whenthe current stimulus matched the stimulus from n steps earlier in thesequence. The value of n can be adjusted to make the task more or lessdifficult. An N-back task at two levels of complexity can be compared toperformance on the Digit Span Test on the Wechsler Adult IntelligenceScale Revised (WAIS-R). Accuracy scores for the N-back task can bepositively correlated with performance on the Digit Span subtest of theWAIS-R. The Digit Span subscale of the WAIS-R can reflect certaincognitive processes, which can overlap with working memory capacity,indicating that accuracy scores on the N-back task can be associatedwith individual differences in working memory capacity.

In a system of the invention, subjects were presented with letters andinstructed to press the space bar when the same letter was shown twoframes earlier. Subjects who were able to identify the second instanceof the letter correctly were assessed as having a high working memory.

The system conducted reliability tests for the N-Back task. Thesplit-half reliability of the N-back test was assessed in a communitysample (N=24), and was found to have above average reliability at aboutr=0.83. A test-retest study used an undergraduate sample (N=38) with atwo-week interval between testing sessions. The reliability was foundacceptable of about r=0.73.

Example 14 Pattern Recognition

The Pattern Recognition task can measure the ability of a subject todiscern patterns and similarities from a sequence of stimuli or objects.

The Raven's Progressive Matrices (RPM) test is similar to the PatternRecognition Task. The Advanced Progressive Matrices (APM) test, which isone form of the Raven's Progressive Matrices test, can have very goodtest-retest reliability. The reliability coefficient can range fromabout 0.76 to about 0.91.

In a system of the invention, the subjects were presented with a grid ofcolored squares with one corner missing. The subjects had to choose animage from six images that would correctly complete the pattern in thegrid, and the subjects who were able to identify the image correctlywere assessed as having high pattern recognition abilities.

The system conducted reliability tests for the Pattern Recognition Task.To assess test-retest reliability, the task was administered to a sampleof undergraduates (N=36) with a two-week interval between sessions. Thereliability was found acceptable at about r=0.55.

Example 15 Reward Learning Task

To assess the relationship between a subject's ability to adjustbehavior as a function of reward, a reward-learning task can bedeveloped wherein subjects earn an amount of money determined by adifferential reinforcement schedule. A subject can be presented with achoice, wherein one choice can be associated with a reward, but receiptof the reward is dependent on picking the correct choice. As a subjectlearns which choice is correct, the reward can increase.

In a system of the inventions, subjects were presented with digitalfaces that either had a short mouth or a long mouth. The difference inlengths of the mouth was minimal, but perceptible by the human eye. Thesubjects were asked to press the right arrow key when presented with theface with the long mouth, and the left arrow key when presented with theface with the short mouth. The subjects were additionally told that theycould receive money if they picked the correct choice. The RewardLearning Task was used to determine whether the subjects were able tolearn which stimulus was correct based upon the receipt of a reward.

The system conducted reliability tests on the Reward Learning Task. Thesplit-half reliability of the reward task was assessed in a communitysample (N=24), and was found to have an above average reliability on akey measure where r=0.78. An undergraduate sample (N=40) was used in atest-retest study with a two-week interval between sessions. Thetest-retest reliability of a key measure was found above average atabout r=0.66.

Example 16 Tower of London (TOL)

The TOL task can be used to assess executive function and planningcapabilities. The mean number of moves and mean initial thinking time(ITT) can be computed for different difficulty levels of the task. TheITT can correspond to elapsed time between the presentation of thepuzzle and the moment when a subject begins solving the puzzle. Negativecorrelations can exist between total mean ITT score and total mean movescore, suggesting that a longer ITT score helps to reduce the number ofmoves, in other words, ITT can reflect planning. Variables measuring thenumber of moves, accurate performance, and time before making the firstmove on Tower of London tasks can have effect sizes of between 0.61 and1.43.

A series of TOL tasks for use in research and clinical settings can beassessed to show a clear and nearly-perfect linear increase of taskdifficulty across minimum moves. In other words, low-, medium-, andhigh-performing subjects can attain correct solutions in problems up toa level of low-, medium-, and high-minimum moves, respectively, but notabove. Accuracy on the task can differ by the number of minimum moves.

In a system of the invention, subjects were presented with two sets ofthree pegs. The target set of pegs had five colored discs around onepeg, while the experimental set of pegs had the five colored discsdistributed across the three pegs. The object of the task was to matchthe arrangement of the colored discs in the experimental set with thatof the target set. Subjects who could complete the task within thespecified time period with the minimum number of moves were assessed ashaving high planning abilities.

The system conducted reliability tests on the TOL task. The split-halfreliability of the TOL task was assessed in a community sample (N=24),and the TOL task was found to have a good reliability for time, a keymeasure, of about r=0.77. A test-retest study using a sample ofundergraduates (N=39) was conducted with a two-week interval betweentest sessions. The reliability for time using this method was foundabove average at about r=0.69.

Example 17 Trust Task

The Trust Task can be used to study trust and reciprocity whilecontrolling for reputation, contractual obligations, or punishment. TheTrust Task can have two stages. First, subjects can be given money andthen the subjects can decide how much, if any, of the money they willsend to an unknown person in a different location. Subjects can be toldthat the amount of money they send will double by the time it reachesthe other person. Then, the other person has the option to send moneyback to the subject.

Performance on the Trust Task can be associated with personalitymeasures including Machiavellianism, and relational motives, forexample, high concern for others and low concern for self. Participationin trust tasks can influence neurophysiological responses, for example,the production of oxytocin, and can be associated with the location,magnitude, and timing of neural responses in areas of the brain relatedto trust and social relationships.

In a system of the invention, subjects were paired with a randomparticipant. The subject received money while the random participantreceived no money. The subjects were instructed to send some, or all, oftheir money to the random participant while knowing that the money wouldtriple by the time the money reached the other person. The other personwas then able to send none, some, or all of the money back to thesubject. The subjects can then assess the fairness of the randomparticipant based on the amount of money they sent back. Subjects whosent more money were perceived as more trusting than those subjects whosent less money to the random participant.

The system conducted reliability tests for the Trust Task. A split-halfreliability study was done with a community sample (N=24) for the TrustTask. The split-half reliability was found reasonable at about r=0.60.The test-retest reliability was measured in a sample of undergraduates(N=40). A key measure was found acceptable at about r=0.59.

TABLE 1 displays a summary of reliability measures calculated in thepreceding examples for the illustrative tasks that can be used by asystem of the invention.

TABLE 1 Test-Retest Split-Half Task Reliability (N) Reliability (N)Analogical Reasoning (Words) .63 (38) Not Tested Balloon Analogue RiskTask .65-.88 (40)    .88-.96 (24)    (Balloons) Choice Task (Choices).62 (40) .82 (24) Dictator Task (Money Exchange 2) .62 (40) .65 (24)Digit Span (Digits) .68 (39) .63 (23) EEfRT (Easy or Hard) .68 (40) .76(24) Facial Affect Test (Faces) .57 (40) .73-.79 (24))    Finger Tapping(Keypresses) .58-.77 (40)    .68-.96 (24)    Flanker Task (Arrows).68-.69 (34)    .71-.76 (14)    Future Discounting (Now or .72 (40) .51(24) Future) Go/No-Go (Stop1) .82 (38) .56 (23) Mind in the Eyes (Eyes).67 (38) .74 (23) N-Back (Letters) .73 (38) .83 (24) Reward LearningTask (Lengths) .66 (40) .78 (24) Tower of London (Towers) .69 (39) .77(24) Trust Task (Money Exchange 1) .59 (40) .60 (24)

Example 18 Use of a System of Invention to Classify Employees

Company A was a consulting firm with 22 employees. The companyidentified four of their employees in this group as top performers,while the other 18 were not identified as top performers. The system wasable to classify employees as bottom or top performers using behavioraldata from the employees' performance on neuroscience tests describedherein using integrated algorithms. The system's algorithms transformedeach employee's set of behavioral data into a fit score that ranged from0-100. The fit scores indicated a likelihood of an employee belonging toone group or another. An individual with a 50% fit score can be equallylikely to be classified as a bottom performer or a top performer,whereas an employee with a 90% fit score can be much more likely to be atrue top performer, and an employee with a 10% fit score can be muchmore likely to be a bottom performer. The system performed binaryclassification while maximizing model accuracy, and the decisionboundary was adjusted to ensure the minimization of false positives andfalse negatives.

The system built a model that correctly identified the four topperformers. The model also classified two bottom performers as topperformers, which means that 16 employees were correctly identified asbottom performers. The system used a decision boundary of 60% tominimize both false positives and false negatives. TABLE 2 displays theresults of this analysis, and indicates how the system's classificationmatched the company's classification. For example, the system classifiedtwo employees as top performers, when, in fact, the company classifiedthose employees as bottom performers. Thus, using a sample of 22individuals, the system built a model that classified the employees with91% accuracy.

TABLE 2 Company Classification Top Performer Bottom Performer System TopPerformer 4 2 Classification Bottom Performer 0 16

Example 19 Use of a System of the Invention to Determine Potential JobPerformance

During a recruiting effort, Company A had 235 individuals apply. Theapplicant pool consisted of undergraduate students matriculating from alarge university. All applicants were assessed both by Company A'sstandard resume review process and by the system's battery of tests. Thesystem was used to increase the efficiency of resume review and toreduce the likelihood of missed talent.

Utilizing the predictive model built in EXAMPLE 18, the system attemptedto identify applicants who were most likely to receive job offers. Tounderstand whether the system's algorithms can increase the yield ofextended offers, the system first compared the number of candidates towhom Company A extended offers versus how many candidates were invitedto interview based on Company A's standard resume review process.Subsequently, the system computed a similar ratio of extended offers tointerviews, based on the system's algorithms in conjunction with CompanyA's standard resume review process (TABLE 3). By utilizing thealgorithms herein in combination with Company A's standard resume reviewprocess, the system increased the yield of extended offers from 5.3% to22.5%

TABLE 3 Total # of Interview Selected to applicants decided by interviewOffers Yield 235 Company A 76 4 5.3% Resume Review Only The system + 184 22.5% Company A Resume Review

Company A also used the system to help reduce missed talent amongapplicants. The company asked the system to recommend 10 applicants fromthe 141 applicants that were rejected by Company A's standard resumereview process. The system was able to match, and slightly exceed, theyield of the company's standard resume review process when evaluatingcandidates that the company rejected by identifying one candidate thatwas offer-worthy among 10 candidates that the system recommended (TABLE4).

TABLE 4 Total # of Company resume Selected to applicants review outcomeinterview Offers Yield 235 Interview Company A 8 8.5% 94 94 No InterviewSystems of the 1  10% 141 Invention 10

Company A also used the system as a service for replacing resume review.The system's algorithms identified 28 of the 235 applicants as beingworthy of an interview. The company interviewed those 28 individuals andextended offers to five of them (TABLE 5). Thus, the system was able toincrease the yield of applicants who were extended offers from 8.5% to17.9%.

TABLE 5 Total # of Interview Selected to applicants decided by interviewOffers Yield 235 Company A 94 84 8.5% Resume Review Only Systems of the28 5 17.9% invention

The system can be utilized for three distinct purposes. The system canincrease the efficiency of resume review by increasing the yield ofapplicants to whom offers are extended. The system can reduce missedtalent by assessing candidates that the company's resume review processdid not otherwise consider. Lastly, the system can be used to replaceresume review in situations when the company does not have the budget tosupport a recruiting team.

Example 20 Use of a System of the Invention to Provide Career Feedback

Company B asked the system to build models to classify employees acrossa range of sales positions as top performers using data from theirperformance on the battery of neuroscience-based tests from a group of782 employees measured over one month. The goal of the analysis was toprovide career development feedback and re-staffing advice, ifnecessary.

The system built models using algorithms to classify employees withineach of the employee positions as either a top performer or a bottomemployee. These models allowed the system to report the traits thatdelineated top from bottom performers. The trait identification featureof the system allowed the system to provide career development advice byquantitatively comparing an individual employee's profile to a modelemployee profile for the company's position and then reporting on theemployee's strengths and areas that need improvement.

Details concerning the number of employees who participated across timeat select intervals are listed in TABLE 6 and represented in FIG. 2. Thefinal group size for top performers from each of the four employeepositions is detailed in TABLE 7.

TABLE 6 Games Completed Day 4 Day 11 Day 18 Day 25 Day 28  0 699 511 230175 173 1-11 23 49 64 71 71 12 33 120 238 263 265 More than 12 27 102250 273 273 Total 782 782 782 782 782

TABLE 7 Employees classified Employee Position as top performers by asystem of the invention Position 1 24 Position 2 37 Position 3 30Position 4 30 Total 121

The model accuracy was determined as follows: CorrectClassification/Total N, where N was the group size and the correctclassification of the employee was determined by the overlap of groupclassification between the system and the company.

Model accuracy results, based on the training data, for the fourpositions examined were all greater than 95% as shown in FIG. 3. FIG. 3depicts a set of 4 histograms, one for each position modeled, and eachhistogram displays the number of employees on the Y-axis and fit scoreson the X-axis. Employees in dark gray whose fit scores were less than0.8 were accurately classified according to the invention's metrics asnot being top performers. Employees depicted in light gray whose fitscores were greater than or equal to 0.8 were accurately classified bythe invention as being top performers. Employees depicted in dark graywhose fit scores were greater than or equal to 0.8 were inaccuratelyclassified as top performers (false positives), while those depicted inlight gray whose scores were less than 0.8 were inaccurately classifiedas not being top performers (false negatives). False positives and falsenegatives were described in section [00115] and depicted in TABLE 2.Company B received a profile analysis by trait for each of the fourmodels built by the system. These profiles suggested traitscharacteristic of a model employee for a specific position.

The system also provided Company B's employees with career developmentfeedback. The system specifically provided each employee with a list ofthe top three traits that make the employee an ideal fit for theirposition, and a list of the top three traits upon which the employeecould improve. In addition, the system provided recommendations as tohow the employee could improve for each trait.

The system classified employees as top performers or bottom performersacross four different sales positions with greater than 95% accuracy.The system was available for re-staffing at Company B because Company Bwas interested in utilizing the results from the system to help transferemployees between departments, if necessary. Furthermore, employeesreceived career development feedback that was directly based on theassessment. The system's assessment specifically identified the traitsof successful employees in a position at the company. The system thengave feedback to the bottom-performing employees about how the employeecompared to the model employee, and ways that the bottom-performingemployee can improve performance.

Example 21 Use of a System of the Invention to Increase the ConversionRate of Temporary Employees

Company C and Company D were consulting firms that recruited heavilyfrom major business schools for summer associates. In 2012 and 2013,Company C employed 57 MBA summer associates, while Company D employed106 student summer associates. A system of the invention assessedstudents that the companies interviewed over the course of two summersand determined whether the system's algorithms could accurately identifystudents who would continue in the consulting field better than thecompany could identify those students. The system built culture fitmodels from students who worked at Company C and Company D, regardlessof the position held. The goal of the study was to increase theconversion rate of summer associates to full-time employees.

Following the summer associate program, Company C extended eight offers,and six of those individuals continued to work in the consultingindustry after finishing school. Company D extended 16 offers, and 11 ofthose individuals continued to work in the consulting industry afterschool ended. The system built models for both Company C and Company Dand generated fit scores to predict to whom the companies should extendoffers. The system suggested that Company C extend offers to 11students, 10 of whom continued to work in the consulting industry. Thesystem also suggested that Company D extend offers to 10 individuals, 9of whom continued to work in the consulting industry (TABLE 8).

TABLE 8 Offers Combined Company Offers Accepted Acceptance RateAcceptance Rate Company C 8 6 75% 71% Company D 16 11 70% System C 10 990% 90% System D 11 10 91%

Example 22 Use of a System of the Invention to Increase Yield ofApplicant Acceptance of Offers

Company C worked with 57 summer associates over 2012 and 2013. Company Cextended offers to 13 of the associates. Ten of the 13 associatesaccepted the offer from Company C. Company C asked the system to testwhether the algorithms could predict who was more likely to accept anoffer from a firm. Using the model previously built for Company C inEXAMPLE 21, the system compared average fit scores for those individualswho accepted an offer from the company to fit scores of thoseindividuals who rejected an offer from the company.

The average fit score of the ten summer associates who accepted afull-time offer from Company C was 69%. The average fit score of thethree individuals who did not accept an offer from Company C was 35%.Thus, the system's fit scores can track individuals who are more likelyto accept an offer from a company. For Company C, individuals whoaccepted Company C's offer had higher culture fit scores than thoseindividuals who rejected Company C's offer.

Example 23 Assessment of Adverse Impact in a System of the Invention

The fit scores created by a system of the invention can be anaggregation of the scores of the individual assessments that are part ofthe system. A multivariate statistical analysis of the fit scores wasdone to evaluate the impact of demographic factors on the scores. Toinvestigate the impact of age on the system's scores, two age groupsfrom the population (N=179), 39-years-old and younger and 40-years-oldand older, were analyzed. The Hotelling's T-squared test was used toassess any statistically significant difference between the age groups.A difference in the groups based on age was not observed. The impact ofage was further analyzed by breaking down the population into four agegroups: a) 29-years-old and younger b) 30-34 c) 35-39, and d)40-years-old or older. A multivariate one-way ANOVA test was employed,which also showed no differences among age groups (p>0.05). Using thesame data set and a Hotelling's T-squared test, the variation betweenfemales and males was not statistically significant (p>0.05). In amultivariate ANOVA test, no significant differences were observed acrossthe race categories (p>>0.1), which included Asian, Black, Hispanic,Middle Eastern, Native American, White, other, and mixed race.

The multivariate statistical analyses demonstrated that none of age,gender, and race was statistically significantly related to the fitscores.

The system can examine the tests for adverse impact by testing for biasin each individual test for differences in results based on age, race,or gender. Results on the system's tests were examined at the individualassessment level. The system examined each task for differences by age,gender, or race groups and the analysis included between one and tenseparate measures for each task. Significant results from thestatistical analysis are given in TABLE 9. None of the tasks showeddifferences by race, and a subset of the tasks showed differences basedon age and gender. For those tasks that showed significant differencesbetween groups, the effect size of those differences was reported. Acorrelation coefficient (r) for the effect size of 0.1 can be consideredsmall; 0.3 can be considered moderate; and 0.5 can be considered large.Sixteen of 17 significant results fell in the small to moderate range,and a single measure from the Tower of London task (time per correctmove) achieved an r of 0.32, in the moderate range.

TABLE 9 Effect size, r Task Results by Age, Gender, or Race (p)Analogical Reasoning No difference by Age, p > 0.14 ns No difference inGender, p > 0.06 ns No difference in Race, p > 0.85 ns Balloon AnalogueRisk No difference by Age, p's > .17 ns Task Risk Taking differed byGender, F(1, 331) = 6.02, p = −0.18 (<.001) 0.01 No difference by Race,p's > 0.38 ns Choice Task Percentage Gamble differed by Age, −0.16(0.003) F(1, 345) = 8.25, p = 0.004 Percentage Gamble differed byGender, F(1, 344) = −0.14 (0.01) 6.77, p = 0.009 No difference by Race,p = 0.80 ns Dictator Task No difference by Age, p's > 0.06 ns Amount 2differed by Gender, F(1, 338) = 3.91, −0.11 (0.05) p < 0.05 Nodifference by Race, p's > 0.28 ns Digit Span No differences by Age, p =0.54 ns No difference by Gender, p = 0.15 ns No difference by Race, p =0.74 ns EEfRT No difference by Age, p's > 0.11 ns Med-High Slopediffered by Gender,  0.14 (0.009) F(1, 336) = 6.89, p = 0.009 Nodifference by Race, p's > 0.06 ns Facial Affect Test Accuracy differedby Age, F(1, 334) = 12.70,  0.19 (<0.001) p < 0.001 No difference byGender, p's > 0.12 ns No difference by Race, p's > 0.24 ns FingerTapping Reaction Time differed by Age, F(1,  0.20 (<0.001) 342) = 12.12,p < 0.001 Reaction Time differed by Gender, F(1, −0.25 (<.001) 340) =21.33, p < 0.001 No difference by Race, p's > 0.99 ns Flanker Task Nodifference by Age, p's > 0.07 ns All Switching, Accuracy differed byGender,  0.15 (0.01) F(1, 284) = 6.71, p = 0.01 No difference by Race,p's > 0.19 ns Future Discounting Discount Rate differed by Age, F(1,330) = 4.07,  0.14 (0.008) p = .04 Discount Rate differed by Gender,F(1, −0.25 (<.001) 330) = 6.24, p = 0.01 No difference by Race, p > 0.79ns Go/No-Go No difference by Age, p's > 0.59 ns No difference by Gender,p's > 0.17 ns No difference by Race, p's > 0.78 ns Mind in the Eyes Nodifference by Age, p > 0.44 ns No difference by Gender, p > 0.60 ns Nodifference by Race, p > 0.85 ns N-Back No difference by Age, p = 0.23 nsAccuracy differed by Gender, F(1,  0.17 (0.002) 332) = 9.65, p = 0.002No difference by Race, p > 0.48 ns Pattern Recognition No difference byAge, p = 0.12 ns Number Correct differed by Gender, F(1, 0.16 (0.003)338) = 9.13, p = 0.003 No difference by Race, p > 0.34 ns RewardLearning Task No difference by Age, p's > 0.41 ns No difference byGender, p's > 0.13 ns No difference by Race, p's > 0.18 ns Tower ofLondon Time per correct move differed by Age, F(1,  0.32 (<0.001) 335) =39.83, p < 0.001 No difference by Gender, p's > 0.64 ns No difference byRace, p's > 0.24 ns Trust Task No difference by Age, p's > 0.12 nsAmount differed by Gender, F(1, 344) = 10.17,  0.17 (0.002) p = 0.001Fairness differed by Gender, F(1, 344) = 7.84, p = −0.15 (0.006) 0.005No difference by Race, p's > 0.06 ns N-Back No difference by Age, p =.23 ns Accuracy differed by Gender, F(1, 332) = 9.65, p =  0.17 (0.002)0.002 No difference by Race, p > 0.48 ns

Balloon Analogue Risk Task (BART)

One measure of the BART showed a significant difference between genders;specifically, women were more risk-averse than men. This differencerepresented 3% of the observed variance explained by gender.

Choice Task

The results differed by both age and gender for the Choice Task. Youngerparticipants had higher percentage gamble scores than participants overthe age of 40. This difference represented 2.6% of the variance for thesample. Examination of percentage gamble by gender revealed that men hadhigher scores than women, and this difference represented 1.96% of thevariance for the sample.

Dictator Task

The amount of money given to the random participant differed by gender,and women gave more in the task than men. This difference represented1.2% of the variance for the sample.

EEfRT

The inflection point after which the more difficult task was chosen morefrequently differed by gender, and men had higher scores than women. Thegender difference explained 1.96% of variance in the data.

Facial Affect Test

The results for the Facial Affect Test differed by age in that olderparticipants were more accurate in identifying emotions from facialexpressions than were younger participants. The age difference explained3.61% of the variance in the data.

Finger Tapping Task

The reaction time for Finger Tapping Task differed by both age andgender. Older participants were slower on the reaction time measure thanyounger participants, and women were slower than men. These effectsaccounted for 4 and 6.25% of variance in the data, respectively.

Flanker Task

One measure of the Flanker Task showed a significant difference betweenmen and women. Men scored higher on switching accuracy, and thisdifference accounted for 2.25% of variance in the data.

Future Discounting

The system identified differences by both age and gender in the FutureDiscounting Task. Older participants were more likely to wait foropportunities in the future than younger participants. This effectaccounted for 1.96% of the variance in the data. The discount rate alsodiffered by gender, in that women were more likely than men to wait foropportunities in the future.

N-Back Test

A measure of accuracy in the N-Back Test differed by gender. Men hadhigher accuracy scores than women, a result that accounted for 2.89% ofvariance in the data.

Trust Task

The system identified differences in both amount and fairness by gender.Men gave a higher amount than women, an effect that accounted for 2.89%of variance in the data. Women gave higher fairness ratings, an effectthat accounted for 2.25% of variance in the data.

Pattern Recognition

The system identified a significant difference based on gender in thePattern Recognition Task. Men had higher pattern recognition scores thanwomen, an effect that accounted for 2.56% of variance in the data.

Towers of London

The system identified a significant effect of age in the Towers ofLondon Task. Older participants took more time per correct move thanyounger participants, an effect that accounted for 10.24% of variance.

Example 24 Fit Score Examination

The system examined sample data for evidence of adverse impact presentwithin the fit scores the system generated for a sample from Company B.TABLE 10 reports the sample demographics, including a breakdown of thesample by position.

The system tested for adverse impact on the total sample (N=464) foreach position. 514 employees from Company B across 4 positions completedthe battery of tests. Individual models were built by the system foreach position from a total sample of 538 employees. The system hadgender data on 464 of the 538 employees. No difference in fit scores wasfound between genders within a position, or across positions.

TABLE 10 p-value for Position N Males Females adverse impact Position 129 12 17 0.41 Position 2 280 154 126 0.79 Position 3 127 53 74 0.13Position 4 28 14 14 0.89 Total 464 233 231 All >0.2

The system did not have access to ethnicity data for the employees ofCompany B reported above. However, the system tested a sample from aninternal database for bias in ethnicity using the models generatedabove. The system generated fit scores for a sample of 962 individualfrom an internal database (TABLE 11). The population consisted of amixture of undergraduate students, MBA students, and industryprofessionals.

TABLE 11 Ethnicity N Caucasian 513 Asian 312 African American 52Hispanic/Latino 85 Total 962

A difference in fit scores between ethnicities was not observed for thesample reported in TABLE 12 (TABLE 12).

TABLE 12 Position F^(a)-statistic p-value Position 1 0.59 0.62 Position2 1.85 0.14 Position 3 2.52 0.06 Position 4 2.45 0.06 ^(a)One-way ANOVA.

Example 25 Fit Score Examination: Industry Fit Models

The system further examined all of the system's industry models forgender and ethnicity bias. The system generated fit scores for a sampleof 962 individuals from an internal database (TABLES 11 and 13). Thepopulation consisted of a mixture of undergraduate students, MBAstudents, and industry professionals. A bias in gender or ethnicity wasnot observed in any of the industry models the system considers stable(TABLE 14).

TABLE 13 Gender N Male 496 Female 496 Total 962

TABLE 14 t-statistic (gender) or F-statistic Model Group (ethnicity)p-value Consulting Gender 0.88 0.35 Consulting Ethnicity 1.55 0.20Education Gender 1.05 0.31 Education Ethnicity 0.62 0.60 EntertainmentGender 0.34 0.56 Entertainment Ethnicity 1.34 0.26 EntrepreneurshipGender 2.05 0.15 Entrepreneurship Ethnicity 0.64 0.59 Finance Gender0.14 0.70 Finance Ethnicity 0.50 0.69 Healthcare Gender 0.62 0.43Healthcare Ethnicity 1.04 0.37 Marketing Gender 0.14 0.70 MarketingEthnicity 1.80 0.15 Product Development Gender 3.23 0.07 ProductDevelopment Ethnicity 0.59 0.62 Project Management Gender 0.86 0.35Project Management Ethnicity 2.31 0.07 Retail Gender 0.49 0.48 RetailEthnicity 1.35 0.26 Hedge Fund Gender 2.41 0.12 Hedge Fund Ethnicity1.85 0.14 Investment Gender 0.15 0.70 Management Investment Ethnicity1.66 0.17 Management Private Equity Gender 0.14 0.71 Private EquityEthnicity 1.70 0.16 Venture Capital Gender 0.30 0.58 Venture CapitalEthnicity 1.88 0.13 Investment Banking Gender 1.64 0.20 InvestmentBanking Ethnicity 1.19 0.31

Example 26 Computer Architectures

Various computer architectures are suitable for use with the invention.FIG. 4 is a block diagram illustrating a first example architecture of acomputer system 400 that can be used in connection with exampleembodiments of the present invention. As depicted in FIG. 4, the examplecomputer system can include a processor 402 for processing instructions.Non-limiting examples of processors include: Intel Core i7™ processor,Intel Core i5™ processor, Intel Core i3™ processor, Intel Xeon™processor, AMD Opteron™ processor, Samsung 32-bit RISC ARM 1176JZ(F)-Sv1.0™ processor, ARM Cortex-A8 Samsung S5PC100™ processor, ARM Cortex-A8Apple A4™ processor, Marvell PXA 930™ processor, or afunctionally-equivalent processor. Multiple threads of execution can beused for parallel processing. In some embodiments, multiple processorsor processors with multiple cores can be used, whether in a singlecomputer system, in a cluster, or distributed across systems over anetwork comprising a plurality of computers, cell phones, and/orpersonal data assistant devices.

Data Acquisition, Processing and Storage.

As illustrated in FIG. 4, a high speed cache 401 can be connected to, orincorporated in, the processor 402 to provide a high speed memory forinstructions or data that have been recently, or are frequently, used byprocessor 402. The processor 402 is connected to a north bridge 406 by aprocessor bus 405. The north bridge 406 is connected to random accessmemory (RAM) 403 by a memory bus 404 and manages access to the RAM 403by the processor 402. The north bridge 406 is also connected to a southbridge 408 by a chipset bus 407. The south bridge 408 is, in turn,connected to a peripheral bus 409. The peripheral bus can be, forexample, PCI, PCI-X, PCI Express, or other peripheral bus. The northbridge and south bridge are often referred to as a processor chipset andmanage data transfer between the processor, RAM, and peripheralcomponents on the peripheral bus 409. In some architectures, thefunctionality of the north bridge can be incorporated into the processorinstead of using a separate north bridge chip.

In some embodiments, system 400 can include an accelerator card 412attached to the peripheral bus 409. The accelerator can include fieldprogrammable gate arrays (FPGAs) or other hardware for acceleratingcertain processing.

Software Interface(s).

Software and data are stored in external storage 413 and can be loadedinto RAM 403 and/or cache 401 for use by the processor. The system 400includes an operating system for managing system resources; non-limitingexamples of operating systems include: Linux, Windows™, MACOS™,BlackBerry OS™, iOS™, and other functionally-equivalent operatingsystems, as well as application software running on top of the operatingsystem.

In this example, system 400 also includes network interface cards (NICs)410 and 411 connected to the peripheral bus for providing networkinterfaces to external storage, such as Network Attached Storage (NAS)and other computer systems that can be used for distributed parallelprocessing.

Computer Systems.

FIG. 5 is a diagram showing a network 500 with a plurality of computersystems 502 a, and 502 b, a plurality of cell phones and personal dataassistants 502 c, and Network Attached Storage (NAS) 501 a, and 501 b.In some embodiments, systems 502 a, 502 b, and 502 c can manage datastorage and optimize data access for data stored in Network AttachedStorage (NAS) 501 a and 502 b. A mathematical model can be used for thedata and be evaluated using distributed parallel processing acrosscomputer systems 502 a, and 502 b, and cell phone and personal dataassistant systems 502 c. Computer systems 502 a, and 502 b, and cellphone and personal data assistant systems 502 c can also provideparallel processing for adaptive data restructuring of the data storedin Network Attached Storage (NAS) 501 a and 501 b. FIG. 5 illustrates anexample only, and a wide variety of other computer architectures andsystems can be used in conjunction with the various embodiments of thepresent invention. For example, a blade server can be used to provideparallel processing. Processor blades can be connected through a backplane to provide parallel processing. Storage can also be connected tothe back plane or as Network Attached Storage (NAS) through a separatenetwork interface.

In some embodiments, processors can maintain separate memory spaces andtransmit data through network interfaces, back plane, or otherconnectors for parallel processing by other processors. In someembodiments, some or all of the processors can use a shared virtualaddress memory space.

Virtual Systems.

FIG. 6 is a block diagram of a multiprocessor computer system using ashared virtual address memory space. The system includes a plurality ofprocessors 601 a-f that can access a shared memory subsystem 602. Thesystem incorporates a plurality of programmable hardware memoryalgorithm processors (MAPs) 603 a-f in the memory subsystem 602. EachMAP 603 a-f can comprise a memory 604 a-f and one or more fieldprogrammable gate arrays (FPGAs) 605 a-f. The MAP provides aconfigurable functional unit and particular algorithms or portions ofalgorithms can be provided to the FPGAs 605 a-f for processing in closecoordination with a respective processor. In this example, each MAP isglobally accessible by all of the processors for these purposes. In oneconfiguration, each MAP can use Direct Memory Access (DMA) to access anassociated memory 604 a-f, allowing it to execute tasks independentlyof, and asynchronously from, the respective microprocessor 601 a-f. Inthis configuration, a MAP can feed results directly to another MAP forpipelining and parallel execution of algorithms.

The above computer architectures and systems are examples only, and awide variety of other computer, cell phone, and personal data assistantarchitectures and systems can be used in connection with exampleembodiments, including systems using any combination of generalprocessors, co-processors, FPGAs and other programmable logic devices,system on chips (SOCs), application specific integrated circuits(ASICs), and other processing and logic elements. Any variety of datastorage media can be used in connection with example embodiments,including random access memory, hard drives, flash memory, tape drives,disk arrays, Network Attached Storage (NAS) and other local ordistributed data storage devices and systems.

In example embodiments, the computer system can be implemented usingsoftware modules executing on any of the above or other computerarchitectures and systems. In other embodiments, the functions of thesystem can be implemented partially or completely in firmware,programmable logic devices such as field programmable gate arrays(FPGAs) as referenced in FIG. 6, system on chips (SOCs), applicationspecific integrated circuits (ASICs), or other processing and logicelements. For example, the Set Processor and Optimizer can beimplemented with hardware acceleration through the use of a hardwareaccelerator card, such as accelerator card 412 illustrated in FIG. 4.

Any embodiment of the invention described herein can be, for example,produced and transmitted by a user within the same geographicallocation. A product of the invention can be, for example, producedand/or transmitted from a geographic location in one country and a userof the invention can be present in a different country. In someembodiments, the data accessed by a system of the invention is acomputer program product that can be transmitted from one of a pluralityof geographic locations 701 to a user 702 (FIG. 7). Data generated by acomputer program product of the invention can be transmitted back andforth among a plurality of geographic locations, for example, by anetwork, a secure network, an insecure network, an internet, or anintranet. In some embodiments, an ontological hierarchy provided by theinvention is encoded on a physical and tangible product.

EMBODIMENTS

The following non-limiting embodiments provide illustrative examples ofthe invention, but do not limit the scope of the invention.

Embodiment 1

A computer program product comprising a computer-readable medium havingcomputer-executable code encoded therein, the computer-executable codeadapted to be executed to implement a method comprising: a) providing arecruitment system, wherein the recruitment system comprises: i) a taskmodule; ii) a measurement module; iii) an assessment module; and iv) anidentification module; b) providing by the task module a computerizedtask to a subject; c) measuring by the measurement module a performancevalue demonstrated by the subject in performance of the task; d)assessing by the assessment module a trait of the subject based on themeasured performance value; and e) identifying to a hiring officer bythe identification module based on the assessed trait that the subjectis suitable for hiring by an entity.

Embodiment 2

The computer program product of embodiment 1, wherein the recruitmentsystem further comprises a profile module, wherein the method furthercomprises creating by the profile module a profile for the subject basedon the assessment of the trait of the subject.

Embodiment 3

The computer program product of any one of embodiments 1-2, wherein therecruitment system further comprises a model module, a reference model,and a comparison module, and wherein the method further comprisesgenerating by the model module a model of the subject based on theassessment of more than one trait of the subject, wherein the methodfurther comprises comparing by the comparison module the model of thesubject and the reference model.

Embodiment 4

The computer program product of any one of embodiments 1-2, wherein therecruitment system further comprises a model module and a comparisonmodule, and wherein the method further comprises generating by the modelmodule a model of the subject based on the assessment of more than onetrait of the subject, wherein the method further comprises comparing bythe comparison module the model of the subject and a database of testsubjects.

Embodiment 5

The computer program product of embodiment 4, wherein the test subjectswork for the entity.

Embodiment 6

The computer program product of any one of embodiments 1-5, wherein thehiring officer works for the entity.

Embodiment 7

The computer program product of embodiment 4, wherein the recruitmentsystem further comprises an aggregation module, wherein the methodfurther comprises collecting by the aggregation module data from thesubject and aggregating the data from the subject into the database ofthe test subjects.

Embodiment 8

The computer program product of embodiment 3, wherein the recruitmentsystem further comprises a scoring module, wherein the method furthercomprises scoring by the scoring module the subject based on thecomparison of the model of the subject and the reference model.

Embodiment 9

The computer program product of embodiment 4, wherein the recruitmentsystem further comprises a scoring module, wherein the method furthercomprises scoring by the scoring module the subject based on thecomparison of the model of the subject with the database of testsubjects.

Embodiment 10

A computer program product comprising a computer-readable medium havingcomputer-executable code encoded therein, the computer-executable codeadapted to be executed to implement a method comprising: a) providing atalent identification system, wherein the talent identification systemcomprises: i) a task module; ii) a measurement module; iii) anassessment module; iv) an identification module; and v) an outputmodule; b) providing by the task module a computerized task to asubject; c) measuring by the measurement module a performance valuedemonstrated by the subject in performance of a task; d) assessing bythe assessment module a trait of the subject based on the measuredperformance value; e) identifying by the identification module a careerpropensity based on the assessing of the trait of subject; and f)outputting by the output module the identified career propensity to ahiring officer.

Embodiment 11

The computer program product of embodiment 10, wherein the talentidentification system further comprises a recommendation module, whereinthe method further comprises recommending by the recommendation module acareer based on the career propensity of the subject.

Embodiment 12

The computer program product of any one of embodiments 10-11, whereinthe talent identification system further comprises a model module, areference model, and a comparison module, and wherein the method furthercomprises generating by the model module a model of the subject based onthe assessment of more than one trait of the subject, wherein the methodfurther comprises comparing by the comparison module the model of thesubject and the reference model.

Embodiment 13

The computer program product of any one of embodiments 10-11, whereinthe talent identification system further comprises a model module and acomparison module, and wherein the method further comprises generatingby the model module a model of the subject based on the assessment ofmore than one trait of the subject, wherein the method further comprisescomparing by the comparison module the model of the subject and adatabase of test subjects.

Embodiment 14

A method comprising: a) providing a computerized task to a subject; b)measuring a performance value demonstrated by the subject in performanceof the task; c) assessing a trait of the subject based on theperformance value; d) comparing by a processor of a computer system thetrait of the subject with a database of test subjects; e) determiningbased on the comparing that the subject is suitable for hiring by anentity; and f) reporting to a hiring officer at the entity that thesubject is suitable for hiring.

Embodiment 15

The method of embodiment 14, further comprising creating a profile forthe subject based on the assessing of the trait of the subject.

Embodiment 16

The method of any one of embodiments 14-15, further comprisinggenerating a model of the subject based on the comparison of more thanone trait of the subject with the database of test subjects.

Embodiment 17

The method of embodiment 16, further comprising scoring the subjectbased on the model of the subject.

Embodiment 18

The method of any one of embodiments 14-17, wherein the assessed traitis a cognitive trait.

Embodiment 19

The method of any one of embodiments 14-18, wherein the assessed traitis an emotional trait.

Embodiment 20

The method of any one of embodiments 14-19, wherein the test subjectswork for the entity.

Embodiment 21

The method of any one of embodiments 14-20, wherein the computerizedtask has an acceptable level of reliability as determined by atest-retest assessment.

Embodiment 22

The method of any one of embodiments 14-21, wherein the computerizedtask has an acceptable level of reliability as determined by asplit-half reliability assessment.

Embodiment 23

A method comprising: a) providing a computerized task to a subject; b)measuring a performance value demonstrated by the subject in performanceof the task; c) assessing a trait of the subject based on theperformance value; d) identifying by a processor of a computer system acareer propensity of the subject based on a comparison of the assessedtrait of the subject with a database of test subjects; and e) outputtinga result of the comparison to a hiring officer.

Embodiment 24

The method of embodiment 23, further comprising creating a profile forthe subject based on the assessing of the trait of the subject.

Embodiment 25

The method of any one of embodiments 23-24, further comprisinggenerating a model for the subject based on comparing more than onetrait of the subject with the database of test subjects.

Embodiment 26

The method of any one of embodiments 23-25, further comprisingrecommending to the subject a career based on the subject's careerpropensity.

Embodiment 27

The method of any one of embodiments 23-26, wherein the computerizedtask has an acceptable level of reliability as determined by atest-retest assessment.

Embodiment 28

The method of any one of embodiments 23-27, wherein the computerizedtask has an acceptable level of reliability as determined by asplit-half reliability assessment.

Embodiment 29

The method of any one of embodiments 23-28, wherein the assessed traitis a cognitive trait.

Embodiment 30

The method of any one of embodiments 23-29, wherein the assessed traitis an emotional trait.

1. A computer-implemented game-based personnel recruitment method,comprising: providing interactive media to a plurality of computingdevices associated with a plurality of participants, wherein theinteractive media comprises at least one recruiting game that isdesigned to measure one or more emotional and cognitive traits of theparticipants, wherein the recruiting game includes a predefined set ofvisual objects associated with a selected neuroscience-based task, andwherein the predefined set of visual objects are displayed to theparticipants on graphical displays of the computing devices; receivinginput data from the computing devices when the participants play therecruiting game on the graphical displays of the computing devices byinteracting with the predefined set of visual objects to complete theselected neuroscience-based task; and analyzing the input data derivedfrom the participants' interaction with the predefined set of visualobjects within the recruiting game to (1) extract measurements of theparticipants' emotional and cognitive traits, and (2) generate astatistics model based on the measurements of the participants'emotional and cognitive traits, wherein the statistical model isrepresentative of a select group of participants, and wherein thestatistical model is used as a reference profile against which acandidate's performance in the recruiting game is measured, in order todetermine the candidate's suitability for recruitment into a targetposition by an entity.
 2. The method of claim 1, further comprising:obtaining the candidate's performance in the recruiting game by:providing the interactive media to a computing device associated withthe candidate; receiving input data from the candidate's computingdevice when the candidate plays the recruiting game on a graphicaldisplay of the candidate's computing device by interacting with thepredefined set of visual objects to complete the selectedneuroscience-based task; and analyzing the input data derived from thecandidate's interaction with the predefined set of visual objects withinthe recruiting game to (1) extract measurements of the candidate'semotional and cognitive traits, and (2) generate a profile of thecandidate based on the measurements of the candidate's emotional andcognitive traits, wherein the candidate's profile is representative ofthe candidate's performance in the recruiting game.
 3. The method ofclaim 2, wherein the candidate's performance in the recruiting game ismeasured against the reference profile by comparing the candidate'sprofile to the reference profile to determine a fit score of thecandidate, and wherein the fit score is indicative of a level of matchof the candidate with the select group of participants.
 4. The method ofclaim 3, further comprising: comparing the candidate's fit score to apredetermined threshold, wherein the predetermined threshold is used asa decision boundary for determining the candidate's suitability forrecruitment into the target position by the entity.
 5. The method ofclaim 4, further comprising: determining that the candidate is in-groupand substantially similar to the select group of participants when thecandidate's fit score is above the predetermined threshold.
 6. Themethod of claim 5, further comprising: determining that the candidate isout-of-group and substantially different from the select group ofparticipants when the candidate's fit score is below the predeterminedthreshold.
 7. The method of claim 2, wherein the plurality ofparticipants are employed by the entity, wherein the select group ofparticipants correspond to a group of employees of the entity who atleast meet a set of job-performance metrics that are predefined by theentity, and wherein the statistical model is correlated with the set ofjob-performance metrics.
 8. (canceled)
 9. The method of claim 1, whereinthe plurality of emotional and cognitive traits of the participants aremeasured over a course of the recruiting game, by evaluating theparticipants' dynamic interactions with the predefined set of visualobjects on the graphical displays of the computing devices to completethe selected neuroscience-based task.
 10. The method of claim 1, whereinthe participants' interactions with the predefined set of visual objectscomprises the participants selecting one or more of the visual objectson the graphical displays to complete the selected neuroscience-basedtask.
 11. The method of claim 1, wherein the participants' interactionswith the predefined set of visual objects comprises the participantsspatially manipulating one or more of the visual objects on thegraphical displays to complete the selected neuroscience-based task. 12.The method of claim 1, wherein the participants' interactions with thepredefined set of visual objects comprises the participants enteringalphanumeric text via one or more of the visual objects on the graphicaldisplays to complete the selected neuroscience-based task.
 13. Themethod of claim 1, wherein the participants interact with the predefinedset of visual objects on the graphical displays using at least one ofthe following input devices: a mouse, a keyboard, or a touchscreenmonitor.
 14. The method of claim 1, wherein the predefined set of visualobjects are provided on the graphical displays in a plurality ofdifferent colors, and wherein the plurality of emotional and cognitivetraits of the participants are measured based on the participants'interactions with the different colored visual objects.
 15. The methodof claim 1, wherein the predefined set of visual objects comprise (1)computer-generated virtual images and/or (2) digital photographs of realpeople and objects.
 16. The method of claim 1, wherein the plurality ofemotional and cognitive traits of the participants are measured based onthe participants' speed, accuracy, and/or judgment in completing theselected neuroscience-based task.
 17. The method of claim 1, wherein therecruiting game is configured to allow the plurality of participants tointeract with one another via the predefined set of visual objects onthe graphical displays to complete the selected neuroscience-based task.18. The method of claim 1, wherein different statistical models aregenerated for a plurality of different fields, functions, industriesand/or entities.
 19. The method of claim 1, wherein the participantsplay the recruiting game on the graphical displays of the computingdevices by interacting with the predefined set of visual objects throughone or more rounds of the game to complete the selectedneuroscience-based task.
 20. A system for implementing a game-basedpersonnel recruitment method, comprising: a server in communication witha plurality of computing devices associated with a plurality ofparticipants, wherein the server comprises a memory for storinginteractive media and a first set of software instructions, and one ormore processors configured to execute the first set of softwareinstructions to: provide the interactive media to the plurality ofcomputing devices associated with the plurality of participants, whereinthe interactive media comprises at least one recruiting game that isdesigned to measure one or more emotional and cognitive traits of theparticipants, wherein the recruiting game includes a predefined set ofvisual objects associated with a selected neuroscience-based task;receive input data from the computing devices when the participants playthe recruiting game on graphical displays of the computing devices byinteracting with the predefined set of visual objects to complete theselected neuroscience-based task; and analyze the input data derivedfrom the participants' interaction with the predefined set of visualobjects within the recruiting game to (1) extract measurements of theparticipants' emotional and cognitive traits, and (2) generate astatistical model based on the measurements of the participants'emotional and cognitive traits, wherein the statistical model isrepresentative of a select group of participants, and wherein thestatistical model is used as a reference profile against which acandidate's performance in the recruiting game is measured, in order todetermine the candidate's suitability for recruitment into a targetposition by an entity; and wherein the plurality of computing devicescomprise a memory for storing a second set of software instructions, andone or more processors configured to execute the second set of softwareinstructions to: receive the interactive media from the server; displaythe recruiting game including the predefined set of visual objectsvisually on the graphical displays of the computing devices to theparticipants; generate the output data when the participants play therecruiting game on the graphical displays of the computing devices byinteracting with the predefined set of visual objects to complete theselected neuroscience-based task; and transmit the input data to theserver for analysis of the input data to generate the statistical model.21. A tangible computer readable medium storing instructions that, whenexecuted by one or more servers, causes the one or more servers toperform a computer-implemented neuroscience-based personnel recruitmentmethod, the method comprising: providing interactive media to aplurality of computing devices associated with a plurality ofparticipants, wherein the interactive media comprises at least onerecruiting game that is designed to measure one or more emotional andcognitive traits of the participants, wherein the recruiting gameincludes a predefined set of visual objects associated with a selectedneuroscience-based task, and wherein the predefined set of visualobjects are presented on graphical displays of the computing devices;receiving input data from the computing devices when the participantsplay the recruiting game on the graphical displays of the computingdevices by interacting with the predefined set of visual objects tocomplete the selected neuroscience-based task; analyzing the input dataderived from the participants' interaction with the predefined set ofvisual objects within the recruiting game to (1) extract measurements ofthe participants' emotional and cognitive traits, and (2) generate astatistical model based on the measurements of the participants'emotional and cognitive traits, wherein the statistical model isrepresentative of a select group of participants; and storing thestatistical model for use by an entity, wherein the statistical model isused as a reference profile against which a candidate's performance inthe recruiting game is measured, in order to determine the candidate'ssuitability for recruitment into a target position by the entity.